package llm_service import ( "ai_scheduler/internal/config" "ai_scheduler/internal/data/model" "ai_scheduler/internal/entitys" "ai_scheduler/internal/pkg" "ai_scheduler/internal/pkg/utils_ollama" "context" "encoding/json" "errors" "fmt" "github.com/gofiber/fiber/v2/log" "github.com/ollama/ollama/api" ) type OllamaService struct { client *utils_ollama.Client config *config.Config } func NewOllamaGenerate( client *utils_ollama.Client, config *config.Config, ) *OllamaService { return &OllamaService{ client: client, config: config, } } func (r *OllamaService) IntentRecognize(ctx context.Context, requireData *entitys.RequireData) (msg string, err error) { prompt, err := r.getPrompt(ctx, requireData) if err != nil { return } toolDefinitions := r.registerToolsOllama(requireData.Tasks) match, err := r.client.ToolSelect(context.TODO(), prompt, toolDefinitions) if err != nil { return } log.Info("意图识别结果: %v", pkg.JsonStringIgonErr(match)) if len(match.Message.Content) == 0 { if match.Message.ToolCalls != nil { var matchFromTools = &entitys.Match{ Confidence: 1, Index: match.Message.ToolCalls[0].Function.Name, Parameters: pkg.JsonStringIgonErr(match.Message.ToolCalls[0].Function.Arguments), IsMatch: true, } match.Message.Content = pkg.JsonStringIgonErr(matchFromTools) } else { err = errors.New("不太明白你想表达的意思呢,可以在仔细描述一下您所需要的内容吗,感谢感谢") return } } msg = match.Message.Content return } func (r *OllamaService) getPrompt(ctx context.Context, requireData *entitys.RequireData) ([]api.Message, error) { var ( prompt = make([]api.Message, 0) ) prompt = append(prompt, api.Message{ Role: "system", Content: buildSystemPrompt(requireData.Sys.SysPrompt), }, api.Message{ Role: "assistant", Content: fmt.Sprintf("聊天记录:%s", pkg.JsonStringIgonErr(buildAssistant(requireData.Histories))), }, api.Message{ Role: "user", Content: requireData.UserInput, }) if len(requireData.ImgByte) > 0 { _, err := r.RecognizeWithImg(ctx, requireData) if err != nil { return nil, err } } return prompt, nil } func (r *OllamaService) RecognizeWithImg(ctx context.Context, requireData *entitys.RequireData) (desc api.GenerateResponse, err error) { requireData.Ch <- entitys.Response{ Index: "", Content: "图片识别中。。。", Type: entitys.ResponseLoading, } desc, err = r.client.Generation(ctx, &api.GenerateRequest{ Model: r.config.Ollama.GenerateModel, Stream: new(bool), System: "识别图片内容", Prompt: requireData.UserInput, }) return } func (r *OllamaService) registerToolsOllama(tasks []model.AiTask) []api.Tool { taskPrompt := make([]api.Tool, 0) for _, task := range tasks { var taskConfig entitys.TaskConfigDetail err := json.Unmarshal([]byte(task.Config), &taskConfig) if err != nil { continue } taskPrompt = append(taskPrompt, api.Tool{ Type: "function", Function: api.ToolFunction{ Name: task.Index, Description: task.Desc, Parameters: api.ToolFunctionParameters{ Type: taskConfig.Param.Type, Required: taskConfig.Param.Required, Properties: taskConfig.Param.Properties, }, }, }) } return taskPrompt }